Language models vs classic machine learning for churn (Hindi)

Choosing an LLM vs classic ML for customer churn prediction is a key product management interview question and architecture choice. Product managers often assume large language models are the smarter pick for predicting when users leave. But running an LLM on tabular data like weekly logins is slow and expensive. Classic machine learning models act like a fast lab machine, scoring thousands of users nightly for pennies using structured numbers. The real advantage of a language model appears when churn signals hide in unstructured text, like angry support tickets. The best setups combine both. The language model reads chats to extract sentiment, turning mood into a numerical feature the cheaper classic model uses for scalable scoring. In this lesson: - Matching the AI tool to your data shape - Why classic machine learning wins for tabular scoring - Using language models to extract text sentiment - Combining both models for cost effective architecture U2xAI Academy - AI skills for product managers. यह लेसन हिंदी में है. This lesson is narrated in Hindi.